{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=6>**优化算法**</font>\n",
    "# Mini-batch梯度下降\n",
    "机器学习是一个不断\"idea-code-experiment\"的过程.这种情况下,算法训练优化的速度显得格外重要,只有速度允许的情况下,我们才能在不长的时间内,迭代尝试不同的idea,进而找到合适的模型.这里讲到的优化算法能够帮助我们快速训练模型.\n",
    "    \n",
    "深度学习在大数据领域能够更好的发挥应有的效果,但是大量的数据必然会降低模型的训练速度.而Mini-batch是这个问题的一个比较有效的解决方式.\n",
    "\n",
    "## **向量化回顾:**\n",
    "\n",
    "$X= \\lbrack x^{(1)}\\ x^{(2)}\\ x^{(3)}\\ldots\\ldots x^{(m)}\\rbrack$\n",
    "\n",
    "$Y= \\lbrack y^{(1)}\\ y^{(2)}\\ y^{(3)}\\ldots \\ldots y^{(m)}\\rbrack$\n",
    "\n",
    "$X$的维数是$(n_{x},m)$，$Y$的维数是$(1,m)$\n",
    "\n",
    "## **Mini-batch:**\n",
    "\n",
    "$\\large X^{\\{t\\}}=\\lbrack x^{(nt+1)}\\dots x^{(n(t+1))}\\rbrack$\n",
    "\n",
    "$\\large Y^{\\{t\\}}=\\lbrack y^{(nt+1)}\\dots y^{(n(t+1))}\\rbrack$\n",
    "\n",
    "相对于mini-batch,我们称全样本参与依次梯度下降为batch,相应的另一个mini-batch的特例是每个mini-batch只有一个样本.\n",
    "<img src=\"images/ddcd8d751da71bdf4a9aa71f0244c5f2.png\" width=50%>\n",
    "## epoch-迭代轮数\n",
    "```python\n",
    "for i in range(epoch_num):\n",
    "    for j in range(batch_t):\n",
    "        ...\n",
    "```\n",
    "<img src=\"images/4be84da3e15341062f3a68d9f8f447c5.png\" width=50%>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 理解mini-batch梯度下降法\n",
    "## **batch与mini-batch的成本值$J$的走势差异**\n",
    "<img src=\"images/b5c07d7dec7e54bed73cdcd43e79452d.png\">\n",
    "\n",
    "1. batch梯度下降,成本值$J$基本上是单调递减的走势.\n",
    "2. mini-batch,成本值$J^{\\{t\\}}$总体呈递减的走势,但是局部存在上下震荡的现象.\n",
    "\n",
    "<img src=\"images/6ffa1351889a85cb5247fc7c43555959.png\">\n",
    "\n",
    "## **mini-batch的两种极端:**\n",
    "1. 如果mini-batch的批次容量$n$为整体样本集$m$,那么mini-batch就变成了batch梯度下降;\n",
    "2. 如果mini-batch的批次容量$n$为$1$,这种情况称为随机梯度下降法,mini-batch每次针对一个样本进行梯度下降;\n",
    "\n",
    "## mini-batch应该如何选择批次容量值?\n",
    "1. 如果训练集数据量$m$比较小(小于2000个样本),直接采用batch梯度下降法即可;\n",
    "2. 否则,mini-batch的批次容量值,推荐设置为64到512等$(2^i,i\\in\\mathbb{Z})$;\n",
    "3. mini-batch批次容量值的设定需要考虑内容的实际情况;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 指数加权平均数\n",
    "1. 简单移动平均:$\\large v_t=\\frac{1}{n}\\sum_{(i=t-n+1)}^{t}{\\theta_i}$\n",
    "2. 指数加权平均:$\\large v_t=\\beta v_{(t-1)}+(1-\\beta)\\theta_t,\\beta\\in(0,1),t>1,v_1=\\theta_1$\n",
    "<img src=\"images/a3b26bbce9cd3d0decba5aa8b26af035.png\">\n",
    "\n",
    "**移动平均:**\n",
    "1. 平滑趋势,减少异常;\n",
    "2. 降低灵敏度;\n",
    "\n",
    "**加权移动较简单移动:**\n",
    "1. 需要较少的内存空间;\n",
    "2. 需要对第一个值特殊处理;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# momentum-动量梯度下降法\n",
    "动量梯度下降法,又叫做momentum,运行速度几乎总是快于标准的梯度下降法,基本思想就是就算梯度的指数加权平均数\n",
    "\n",
    "**原理:**\n",
    "<img src=\"images/cc2d415b8ccda9fdaba12c575d4d3c4b.png\">\n",
    "1. momentum保留下降方向的趋势速度,非下降方向经平均之后值变小;\n",
    "\n",
    "\n",
    "**公式:**\n",
    "<img src=\"images/a3af0fb5e49e16c108e72d015ef0fdb6.png\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSprop\n",
    "RMSprop,全称是root mean square prop,即均方根传播算法.\n",
    "\n",
    "**原理:**\n",
    "<img src=\"images/cc2d415b8ccda9fdaba12c575d4d3c4b.png\">\n",
    "\n",
    "**公式:**\n",
    "\n",
    "$S_{dW}= \\beta S_{dW} + (1 -\\beta) {(dW)}^{2}$\n",
    "\n",
    "$S_{db}= \\beta S_{db} + (1 - \\beta){(db)}^{2}$\n",
    "\n",
    "$W:= W -a\\frac{dW}{\\sqrt{S_{dW}}}$\n",
    "\n",
    "$b:=b -\\alpha\\frac{db}{\\sqrt{S_{db}}}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adam\n",
    "**Adam**优化算法基本上就是将**Momentum**和**RMSprop**结合在一起,全称为Adaptive Moment Estimation,即自适应动量估计算法.\n",
    "\n",
    "**公式:**\n",
    "\n",
    "$v_{dW}= \\beta_1 v_{dW} + (1 -\\beta_1) {(dW)},v_{db}= \\beta_1 v_{db} + (1 -\\beta_1) {(db)}$\n",
    "\n",
    "$S_{dW}= \\beta_2 S_{dW} + (1 -\\beta_2) {(dW)}^{2},S_{db}= \\beta_2 S_{db} + (1 - \\beta_2){(db)}^{2}$\n",
    "\n",
    "$W:= W -a\\frac{v_{dW}}{\\sqrt{S_{dW}+\\epsilon}}$\n",
    "\n",
    "$b:=b -\\alpha\\frac{v_{db}}{\\sqrt{S_{db}+\\epsilon}}$\n",
    "\n",
    "**超参数,推荐配置:**\n",
    "1. $\\beta_1=0.9$;\n",
    "2. $\\beta_2=0.999$;\n",
    "3. $\\epsilon=10^{-8}$;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 学习率衰减\n",
    "**学习率衰减的优势:**\n",
    "<img src=\"images/095feaa609b0029d6abc5c74ef7b3b35.png\">\n",
    "\n",
    "**实现:**\n",
    "\n",
    "mini-batch中迭代轮数增加,学习率衰减.\n",
    "```python\n",
    "for i in range(epoch):\n",
    "    a:=a-x\n",
    "    ...\n",
    "```\n",
    "\n",
    "**学习率衰减的方法:**\n",
    "1. $\\large\\alpha:=\\frac{1}{1+epoch\\_num*decay\\_rate}\\alpha_0$;\n",
    "2. $\\large\\alpha:=\\gamma^{epoch\\_num}\\alpha_0,\\gamma\\in(0,1)$;\n",
    "3. $\\large\\alpha:=\\frac{k}{\\sqrt{epoch\\_num}}\\alpha_0,或者\\alpha:=\\frac{k}{\\sqrt{t}}\\alpha_0$\n",
    "4. $\\large\\alpha:=\\frac{1}{2^{epoch\\_num}}\\alpha_0$\n",
    "5. 手动设置"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 优化算法-练习\n",
    "接下来我们来练习使用本节学到的优化算法来提高模型学习速度和效果.\n",
    "## 环境准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io\n",
    "import math\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 梯度下降\n",
    "机器学习中一个简单而又重要的方法是梯度下降.当我们一次迭代梯度下降利用所有样本的时候,称之为批处理梯度下降,也是mini-batch的一种特殊形式.\n",
    "\n",
    "**公式:**\n",
    "$$ W^{[l]} = W^{[l]} - \\alpha \\text{ } dW^{[l]} \\tag{1}$$\n",
    "$$ b^{[l]} = b^{[l]} - \\alpha \\text{ } db^{[l]} \\tag{2}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_parameters_with_gd(parameters, grads, learning_rate):\n",
    "    L = len(parameters) // 2\n",
    "    for l in range(L):\n",
    "        parameters[\"W\" + str(l+1)] = parameters[\"W\" + str(l+1)] - learning_rate*grads[\"dW\" + str(l+1)]\n",
    "        parameters[\"b\" + str(l+1)] = parameters[\"b\" + str(l+1)] -learning_rate*grads[\"db\" + str(l+1)]        \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "梯度下降的一个变体是随机梯度下降(SGD),相当于mini-batch梯度下降时批次容量为一个样本的特例.\n",
    "\n",
    "- **梯度下降-Batch**:\n",
    "``` python\n",
    "X = data_input\n",
    "Y = labels\n",
    "parameters = initialize_parameters(layers_dims)\n",
    "for i in range(0, num_iterations):\n",
    "    a, caches = forward_propagation(X, parameters)\n",
    "    cost = compute_cost(a, Y)\n",
    "    grads = backward_propagation(a, caches, parameters)\n",
    "    parameters = update_parameters(parameters, grads)    \n",
    "```\n",
    "\n",
    "- **随机梯度下降-SGD-Stochastic Gradient Descent**:\n",
    "```python\n",
    "X = data_input\n",
    "Y = labels\n",
    "parameters = initialize_parameters(layers_dims)\n",
    "for i in range(0, num_iterations):\n",
    "    for j in range(0, m):\n",
    "        a, caches = forward_propagation(X[:,j], parameters)\n",
    "        cost = compute_cost(a, Y[:,j])\n",
    "        grads = backward_propagation(a, caches, parameters)\n",
    "        parameters = update_parameters(parameters, grads)\n",
    "```\n",
    "\n",
    "**梯度下降走势对比:**\n",
    "<img src=\"images/b546825d9b3cd39c791899d67fdc4cfd.png\" style=\"width:750px;height:250px;\">\n",
    "\n",
    "**小结:**\n",
    "1. batch,mini-batch和sgd的区别和联系;\n",
    "2. batch适用于数据量较小的情况;\n",
    "3. sgd适用于数据异常不显著即样本数据平滑的情况;\n",
    "4. 大多数情况应该考虑使用mini-batch,应该选择合适的批次容量值;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## mini-batch梯度下降\n",
    "### 清洗\n",
    "随机排列样本(X,Y)的顺序,需要保持特征样本X和标签样本Y的的对应关系,即同步随机重排样本(X,Y)的顺序.这样确保样本随机的分到不同的mini-batch中.\n",
    "<img src=\"images/30d4903af08c34ae69f6b68e00710260.png\" width=80%>\n",
    "\n",
    "### 划分\n",
    "划分随机重排顺序的样本称为mini-batch.\n",
    "<img src=\"images/ec350bd13f4b35e4666487ca7eab2cb3.png\" width=80%>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_mini_batches(X, Y, mini_batch_size = 64, seed = 0):    \n",
    "    np.random.seed(seed)\n",
    "    m = X.shape[1]\n",
    "    mini_batches = []\n",
    "        \n",
    "    # 第一步,清洗重排\n",
    "    permutation = list(np.random.permutation(m))\n",
    "    shuffled_X = X[:, permutation]\n",
    "    shuffled_Y = Y[:, permutation].reshape((1,m))\n",
    "\n",
    "    # 第二步,划分\n",
    "    num_complete_minibatches = math.floor(m/mini_batch_size)\n",
    "    for k in range(0, num_complete_minibatches):\n",
    "        mini_batch_X = shuffled_X[:, k * mini_batch_size : (k+1) * mini_batch_size]\n",
    "        mini_batch_Y = shuffled_Y[:, k * mini_batch_size : (k+1) * mini_batch_size]\n",
    "        mini_batch = (mini_batch_X, mini_batch_Y)\n",
    "        mini_batches.append(mini_batch)\n",
    "    # 样本数量不能整除批次容量值\n",
    "    if m % mini_batch_size != 0:\n",
    "        mini_batch_X = shuffled_X[:, num_complete_minibatches * mini_batch_size : m]\n",
    "        mini_batch_Y = shuffled_Y[:, num_complete_minibatches * mini_batch_size : m]\n",
    "        mini_batch = (mini_batch_X, mini_batch_Y)\n",
    "        mini_batches.append(mini_batch)\n",
    "    return mini_batches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Momentum\n",
    "mini-batch每一次的梯度下降是基于一个批次的样本,这相较于batch参数更新会出现误差,梯度下降的路径会出现震荡.这时候使用momentum可以降低震荡."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_parameters_with_momentum(parameters, grads, v, beta, learning_rate):\n",
    "    L = len(parameters) // 2\n",
    "    for l in range(L):\n",
    "        v[\"dW\" + str(l + 1)] = beta*v[\"dW\" + str(l + 1)]+(1-beta)*grads['dW' + str(l+1)]\n",
    "        v[\"db\" + str(l + 1)] = beta*v[\"db\" + str(l + 1)]+(1-beta)*grads['db' + str(l+1)]\n",
    "        parameters[\"W\" + str(l + 1)] = parameters['W' + str(l+1)] - learning_rate*v[\"dW\" + str(l + 1)] \n",
    "        parameters[\"b\" + str(l + 1)] = parameters['b' + str(l+1)] - learning_rate*v[\"db\" + str(l + 1)]         \n",
    "    return parameters, v\n",
    "\n",
    "def initialize_velocity(parameters):    \n",
    "    L = len(parameters) // 2\n",
    "    v = {}   \n",
    "    for l in range(L):\n",
    "        v[\"dW\" + str(l+1)] = np.zeros(parameters['W' + str(l+1)].shape)\n",
    "        v[\"db\" + str(l+1)] = np.zeros(parameters['b' + str(l+1)].shape)        \n",
    "    return v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**小结:**\n",
    "1. momentum能够平滑梯度下降的路径,通过移动平均平滑的方法,增强正确方向的速度,减小错误方向的速度;\n",
    "1. momentum的参数$\\beta$越大,移动平均值的平滑度越高,但是如果参数设置太大,那么最新的微分值就不会起到应有的作用;\n",
    "2. 通常参数$\\beta\\in[0.8,0.999]$,如果不想调优该参数,一般使用默认值$\\beta=0.9$;\n",
    "3. 参数调优,遵循\"idea-code-experiment\"的原则;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adam\n",
    "Adam是训练神经网络最有效的优化算法之一.Adam是由RMSProp和Momentum综合而成.\n",
    "\n",
    "**公式:**\n",
    "\n",
    "$$\\large\\begin{cases}\n",
    "v_{dW^{[l]}} = \\beta_1 v_{dW^{[l]}} + (1 - \\beta_1) \\frac{\\partial \\mathcal{J} }{ \\partial W^{[l]} } \\\\\n",
    "v^{corrected}_{dW^{[l]}} = \\frac{v_{dW^{[l]}}}{1 - (\\beta_1)^t} \\\\\n",
    "s_{dW^{[l]}} = \\beta_2 s_{dW^{[l]}} + (1 - \\beta_2) (\\frac{\\partial \\mathcal{J} }{\\partial W^{[l]} })^2 \\\\\n",
    "s^{corrected}_{dW^{[l]}} = \\frac{s_{dW^{[l]}}}{1 - (\\beta_1)^t} \\\\\n",
    "W^{[l]} = W^{[l]} - \\alpha \\frac{v^{corrected}_{dW^{[l]}}}{\\sqrt{s^{corrected}_{dW^{[l]}}} + \\varepsilon}\n",
    "\\end{cases}$$\n",
    "\n",
    "**其中:**\n",
    "\n",
    "- t 神经网络迭代的次数;\n",
    "- L 神经网络的层数\n",
    "- $\\beta_1$ , $\\beta_2$ 是分别控制Momentum和RMSProp的移动平均加权因子的超参数;\n",
    "- $\\alpha$ 是学习率;\n",
    "- $\\varepsilon$ 是避免除数为零的超参数值,一般使用默认值$10^{-8}即可$;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def update_parameters_with_adam(parameters, grads, v, s, t, learning_rate = 0.01,\\\n",
    "                                beta1 = 0.9, beta2 = 0.999,  epsilon = 1e-8):\n",
    "    \n",
    "    L = len(parameters) // 2    \n",
    "    v_corrected = {}                         \n",
    "    s_corrected = {}                         \n",
    "    for l in range(L):\n",
    "        v[\"dW\" + str(l + 1)] = beta1*v[\"dW\" + str(l + 1)] +(1-beta1)*grads['dW' + str(l+1)]\n",
    "        v[\"db\" + str(l + 1)] = beta1*v[\"db\" + str(l + 1)] +(1-beta1)*grads['db' + str(l+1)]\n",
    "\n",
    "        v_corrected[\"dW\" + str(l + 1)] = v[\"dW\" + str(l + 1)]/(1-(beta1)**t)\n",
    "        v_corrected[\"db\" + str(l + 1)] = v[\"db\" + str(l + 1)]/(1-(beta1)**t)\n",
    "\n",
    "        s[\"dW\" + str(l + 1)] =beta2*s[\"dW\" + str(l + 1)] + (1-beta2)*(grads['dW' + str(l+1)]**2)\n",
    "        s[\"db\" + str(l + 1)] = beta2*s[\"db\" + str(l + 1)] + (1-beta2)*(grads['db' + str(l+1)]**2)\n",
    "\n",
    "        s_corrected[\"dW\" + str(l + 1)] =s[\"dW\" + str(l + 1)]/(1-(beta2)**t)\n",
    "        s_corrected[\"db\" + str(l + 1)] = s[\"db\" + str(l + 1)]/(1-(beta2)**t)\n",
    "\n",
    "        parameters[\"W\" + str(l + 1)] = parameters[\"W\" + str(l + 1)]-\\\n",
    "        learning_rate*(v_corrected[\"dW\" + str(l + 1)]/np.sqrt( s_corrected[\"dW\" + str(l + 1)]+epsilon))\n",
    "        parameters[\"b\" + str(l + 1)] = parameters[\"b\" + str(l + 1)]-\\\n",
    "        learning_rate*(v_corrected[\"db\" + str(l + 1)]/np.sqrt( s_corrected[\"db\" + str(l + 1)]+epsilon))\n",
    "    return parameters, v, s\n",
    "def initialize_adam(parameters) :    \n",
    "    L = len(parameters) // 2\n",
    "    v = {}\n",
    "    s = {}\n",
    "\n",
    "    for l in range(L):\n",
    "        v[\"dW\" + str(l + 1)] = np.zeros(parameters[\"W\" + str(l+1)].shape)\n",
    "        v[\"db\" + str(l + 1)] = np.zeros(parameters[\"b\" + str(l+1)].shape)\n",
    "        s[\"dW\" + str(l + 1)] = np.zeros(parameters[\"W\" + str(l+1)].shape)\n",
    "        s[\"db\" + str(l + 1)] = np.zeros(parameters[\"b\" + str(l+1)].shape)    \n",
    "    return v, s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 不同优化算法的效果\n",
    "### 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def load_dataset():\n",
    "    np.random.seed(3)\n",
    "    train_X, train_Y = sklearn.datasets.make_moons(n_samples=300, noise=.2) #300 #0.2 \n",
    "    # Visualize the data\n",
    "    plt.scatter(train_X[:, 0], train_X[:, 1], c=train_Y, s=40, cmap=plt.cm.Spectral);\n",
    "    train_X = train_X.T\n",
    "    train_Y = train_Y.reshape((1, train_Y.shape[0]))\n",
    "    return train_X, train_Y\n",
    "train_X, train_Y = load_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(x):\n",
    "    s = 1/(1+np.exp(-x))\n",
    "    return s\n",
    "def relu(x):\n",
    "    s = np.maximum(0,x)    \n",
    "    return s\n",
    "def initialize_parameters(layer_dims):    \n",
    "    np.random.seed(3)\n",
    "    parameters = {}\n",
    "    L = len(layer_dims)\n",
    "    for l in range(1, L):\n",
    "        parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1])*  np.sqrt(2 / layer_dims[l-1])\n",
    "        parameters['b' + str(l)] = np.zeros((layer_dims[l], 1))        \n",
    "    return parameters\n",
    "def forward_propagation(X, parameters):\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    W3 = parameters[\"W3\"]\n",
    "    b3 = parameters[\"b3\"]\n",
    "    \n",
    "    # LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID\n",
    "    z1 = np.dot(W1, X) + b1\n",
    "    a1 = relu(z1)\n",
    "    z2 = np.dot(W2, a1) + b2\n",
    "    a2 = relu(z2)\n",
    "    z3 = np.dot(W3, a2) + b3\n",
    "    a3 = sigmoid(z3)\n",
    "    \n",
    "    cache = (z1, a1, W1, b1, z2, a2, W2, b2, z3, a3, W3, b3)    \n",
    "    return a3, cache\n",
    "def compute_cost(a3, Y):\n",
    "    m = Y.shape[1]\n",
    "    logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n",
    "    cost = 1./m * np.sum(logprobs)  \n",
    "    return cost\n",
    "def backward_propagation(X, Y, cache):\n",
    "    m = X.shape[1]\n",
    "    (z1, a1, W1, b1, z2, a2, W2, b2, z3, a3, W3, b3) = cache\n",
    "    \n",
    "    dz3 = 1./m * (a3 - Y)\n",
    "    dW3 = np.dot(dz3, a2.T)\n",
    "    db3 = np.sum(dz3, axis=1, keepdims = True)\n",
    "    \n",
    "    da2 = np.dot(W3.T, dz3)\n",
    "    dz2 = np.multiply(da2, np.int64(a2 > 0))\n",
    "    dW2 = np.dot(dz2, a1.T)\n",
    "    db2 = np.sum(dz2, axis=1, keepdims = True)\n",
    "    \n",
    "    da1 = np.dot(W2.T, dz2)\n",
    "    dz1 = np.multiply(da1, np.int64(a1 > 0))\n",
    "    dW1 = np.dot(dz1, X.T)\n",
    "    db1 = np.sum(dz1, axis=1, keepdims = True)\n",
    "    \n",
    "    gradients = {\"dz3\": dz3, \"dW3\": dW3, \"db3\": db3,\n",
    "                 \"da2\": da2, \"dz2\": dz2, \"dW2\": dW2, \"db2\": db2,\n",
    "                 \"da1\": da1, \"dz1\": dz1, \"dW1\": dW1, \"db1\": db1}\n",
    "    \n",
    "    return gradients\n",
    "def predict(X, y, parameters):    \n",
    "    m = X.shape[1]\n",
    "    p = np.zeros((1,m), dtype = np.int)\n",
    "    a3, caches = forward_propagation(X, parameters)\n",
    "    for i in range(0, a3.shape[1]):\n",
    "        if a3[0,i] > 0.5:\n",
    "            p[0,i] = 1\n",
    "        else:\n",
    "            p[0,i] = 0\n",
    "    print(\"Accuracy: \"  + str(np.mean((p[0,:] == y[0,:]))))  \n",
    "    return p\n",
    "def predict_dec(parameters, X):    \n",
    "    a3, cache = forward_propagation(X, parameters)\n",
    "    predictions = (a3 > 0.5)\n",
    "    return predictions\n",
    "def plot_decision_boundary(model, X, y):\n",
    "    x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1\n",
    "    y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1\n",
    "    h = 0.01\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "    Z = model(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n",
    "    plt.ylabel('x2')\n",
    "    plt.xlabel('x1')\n",
    "    plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral)\n",
    "    plt.show()\n",
    "def model(X, Y, layers_dims, optimizer, learning_rate = 0.0007, mini_batch_size = 64, beta = 0.9,\n",
    "          beta1 = 0.9, beta2 = 0.999,  epsilon = 1e-8, num_epochs = 10000, print_cost = True):\n",
    "    L = len(layers_dims)\n",
    "    costs = []                       \n",
    "    t = 0                            \n",
    "    seed = 10                        \n",
    "    parameters = initialize_parameters(layers_dims)\n",
    "    if optimizer == \"gd\":\n",
    "        pass \n",
    "    elif optimizer == \"momentum\":\n",
    "        v = initialize_velocity(parameters)\n",
    "    elif optimizer == \"adam\":\n",
    "        v, s = initialize_adam(parameters)\n",
    "    \n",
    "    for i in range(num_epochs):\n",
    "        seed = seed + 1\n",
    "        minibatches = random_mini_batches(X, Y, mini_batch_size, seed)\n",
    "\n",
    "        for minibatch in minibatches:\n",
    "            (minibatch_X, minibatch_Y) = minibatch\n",
    "            a3, caches = forward_propagation(minibatch_X, parameters)\n",
    "            cost = compute_cost(a3, minibatch_Y)\n",
    "            grads = backward_propagation(minibatch_X, minibatch_Y, caches)\n",
    "            if optimizer == \"gd\":\n",
    "                parameters = update_parameters_with_gd(parameters, grads, learning_rate)\n",
    "            elif optimizer == \"momentum\":\n",
    "                parameters, v = update_parameters_with_momentum(parameters, grads, v, beta, learning_rate)\n",
    "            elif optimizer == \"adam\":\n",
    "                t = t + 1\n",
    "                parameters, v, s = update_parameters_with_adam(parameters, grads, v, s,\n",
    "                                                               t, learning_rate, beta1, beta2,  epsilon)\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print (\"Cost after epoch %i: %f\" %(i, cost))\n",
    "        if print_cost and i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "\n",
    "    plt.plot(costs)\n",
    "    plt.ylabel('cost')\n",
    "    plt.xlabel('epochs (per 100)')\n",
    "    plt.title(\"Learning rate = \" + str(learning_rate))\n",
    "    plt.show()\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### mini-batch梯度下降"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after epoch 0: 0.690736\n",
      "Cost after epoch 1000: 0.685273\n",
      "Cost after epoch 2000: 0.647072\n",
      "Cost after epoch 3000: 0.619525\n",
      "Cost after epoch 4000: 0.576584\n",
      "Cost after epoch 5000: 0.607243\n",
      "Cost after epoch 6000: 0.529403\n",
      "Cost after epoch 7000: 0.460768\n",
      "Cost after epoch 8000: 0.465586\n",
      "Cost after epoch 9000: 0.464518\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7966666666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "layers_dims = [train_X.shape[0], 5, 2, 1]\n",
    "parameters = model(train_X, train_Y, layers_dims, optimizer = \"gd\")\n",
    "predictions = predict(train_X, train_Y, parameters)\n",
    "plt.title(\"Model with Gradient Descent optimization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,2.5])\n",
    "axes.set_ylim([-1,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Mini-batch梯度下降+momentum优化算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after epoch 0: 0.690741\n",
      "Cost after epoch 1000: 0.685341\n",
      "Cost after epoch 2000: 0.647145\n",
      "Cost after epoch 3000: 0.619594\n",
      "Cost after epoch 4000: 0.576665\n",
      "Cost after epoch 5000: 0.607324\n",
      "Cost after epoch 6000: 0.529476\n",
      "Cost after epoch 7000: 0.460936\n",
      "Cost after epoch 8000: 0.465780\n",
      "Cost after epoch 9000: 0.464740\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7966666666666666\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "layers_dims = [train_X.shape[0], 5, 2, 1]\n",
    "parameters = model(train_X, train_Y, layers_dims, beta = 0.9, optimizer = \"momentum\")\n",
    "predictions = predict(train_X, train_Y, parameters)\n",
    "plt.title(\"Model with Momentum optimization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,2.5])\n",
    "axes.set_ylim([-1,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mini-batch梯度下降+Adam优化算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after epoch 0: 0.690552\n",
      "Cost after epoch 1000: 0.185501\n",
      "Cost after epoch 2000: 0.150830\n",
      "Cost after epoch 3000: 0.074454\n",
      "Cost after epoch 4000: 0.125959\n",
      "Cost after epoch 5000: 0.104344\n",
      "Cost after epoch 6000: 0.100676\n",
      "Cost after epoch 7000: 0.031652\n",
      "Cost after epoch 8000: 0.111973\n",
      "Cost after epoch 9000: 0.197940\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.94\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "layers_dims = [train_X.shape[0], 5, 2, 1]\n",
    "parameters = model(train_X, train_Y, layers_dims, optimizer = \"adam\")\n",
    "predictions = predict(train_X, train_Y, parameters)\n",
    "plt.title(\"Model with Adam optimization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,2.5])\n",
    "axes.set_ylim([-1,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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   "nbconvert_exporter": "python",
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   "version": "3.6.5"
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  "toc": {
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   "toc_position": {},
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  "varInspector": {
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   "kernels_config": {
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